1. Field of the Invention
The present invention relates to a machine, method and medium for allocating resources over a plurality of time periods, taking into account a plurality of interrelated processes such as marketing, procurement and production.
2. Related Art
Today's businesses generally includes a procurement process, production process, and marketing process. The standard practice of these businesses is to optimize each of these processes separately with little or no regard to interdependencies among processes. Individual process decision is made in a disjointed manner with no consideration to the total business operation. Though manual handoffs exist, it is very poorly linked and contrary to best-in-class technology. This has created problems of inefficiency in most large and mid-size corporations. In addition to a lack of consideration of sufficient interdependencies, each process is optimized separately using static optimization techniques such that a system (comprising products or services made up of various resources and configured based upon customer demand) designed for a given time period t is not interdependent with systems designed for, e.g., times t+1 or t-1. In effect, the problem with the static optimization causes a business process to make reactive business decisions as opposed to proactive business decisions.
Thus, the problem existing with the majority of optimization tools existing today in industries is that they are based on static optimization algorithms that do not reflect true costs or provide any competitive advantage in the face of fierce competition in various markets. This is because the competition influences the elasticity of demand and price and makes the overall business environment very dynamic. For example, fluctuating demand in retail industries has a continuous impact on the shipping schedule among suppliers, warehouses and retail stores. In the auto industry, changes in demand at the sales outlets have a bearing on the production floor schedules and procurement of spare parts. However, the frequency of change and variables of change differ from one industry to the other. In telecommunication services industry, changes in demand similarly have an impact on the existing capacity, procurement of additional capacity or production operation.
As competition rises in markets in the coming decades, companies will demand more real time information of resource costs and competitive pricing. Static optimization, however, only yields a cost-optimal system in a snap-shot of time, usually at present, without regard to future growth or decline in any service type, time value of money, introduction of new technology or change in customer demographics. The problem with the static, optimization is that what is optimum today may not be optimum tomorrow. It causes a business to make reactive business decisions as opposed to proactive business decisions. It also relies heavily on human judgement and involvement and calls for creation of many unnecessary subprocesses within a business process or many unnecessary processes within a business. Business decisions that are made based on many of these business processes, are poor and sub-optimal with respect to a multi-year planning horizon. Specifically, when a static optimization technique is run at time period zero (i.e. now), it cannot solve resource rearrangement problems for future time periods.
It is true that current static optimization techniques can analyze a future time period as if it were the current time period (i.e. time period t=0). However, this still only deals with a single period, and does not take into account that effect that other time periods will have on it. Being in the current time period, this technique cannot produce optimal decisions that will occur at future time periods. Nonetheless, utilization of such static techniques have been the standard practice of businesses and industries. For example, retail, manufacturing, telecommunications and service industries rely heavily on static optimization techniques.
In the airline industry, an optimum fleet schedule is very important to the financial well-being of this industry. Efforts to enhance efficiency and charge "the right" price for seats have been the subject of reports such as "Yield Management at American Airlines" by Smith et al. However these reports (and the airline industry, generally), nonetheless applies mostly static optimization techniques. This leaves them vulnerable to the inelement business conditions for future time periods. Knowing about optimal business activities of the future time periods will put any business at a competitive edge. This is why a static optimization is not adequate in fiercely competitive marketplace.
Some industries referred to above that do not apply strictly "static" optimization instead utilize "pseudo-dynamic" optimization which is sometimes misconstrued as true dynamic optimization. Though the pseudo-dynamic optimization is also based on a multi-period planning horizon, many characteristics of static optimization heavily bias the results of this pseudo-dynamic optimization. For example, the output of static optimization is used as input to pseudo-dynamic optimization. Sometimes the results of static optimization are used to approximate the future activities at a very macro-level which can be significantly sub-optimal. This again calls for creation of additional business processes and more human intervention to respond to the business conditions in more reactive manner.
Thus, what is needed is a scheme for using dynamic optimization techniques such that systems contemplated for implementation at various times are interrelated, and that the procurement, production and marketing processes are interrelated as well.